Wireless communication systems have become a prevalent means by which a majority of people worldwide have come to communicate. This is due in large part to the fact that recent advances in wireless communication technology have considerably improved the ability of such systems to carry data relating to voice, video, packet data, broadcast, messaging, and other services used in communication. In particular, multiple-input multiple-output (MIMO) communication systems are receiving increased attention due to their ability to improve the capacity of a wireless communication system through the use of multiple antennas for simultaneously transmitting or receiving data. Using a MIMO communication system, data may be divided into multiple streams, which may be sent or received simultaneously to improve system capacity without requiring significant additional spectrum or power.
In typical MIMO communication systems, data is transmitted by dividing the data into streams, grouping bits in each stream, mapping each group of bits to constellation points, and then transmitting the streams via multiple transmit antennas as modulated carrier waves based on the constellation points mapped for each stream. Once transmitted, the data passes through an effective MIMO channel, after which resulting spatial streams are received by multiple antennas at a receiver. Conventional MIMO receivers then employ a variety of signal detection techniques to obtain data from streams received at receive antennas. One such technique, Soft-Output Maximum-Likelihood Detection (SOMLD), may obtain the expected value of a detected transmitted bit as well as the likelihood that the expected value is correct. Conventional SOMLD techniques require looping over all constellation points used by the transmitter for each transmitted stream and determining a distance metric for each constellation point to find the likelihood of each bit in the streams. However, to determine optimal distance metrics in conventional SOMLD, additional looping is required over all constellation points for all other streams, effectively requiring looping over all possible combinations of constellation points for all streams. This procedure has exponential computational complexity, which makes it prohibitively costly for many applications, including applications that could benefit from soft-output detection. Thus, there exists a need in the art for low-complexity techniques that achieve Maximum-Likelihood-Detection (MLD) performance or near-MLD performance for hard-decision output detection or that achieve SOMLD performance or near-SOMLD performance for soft decision output signal detection in MIMO communication systems.